
"""simple docstring"""



import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


a       :						Union[str, Any]       		=		{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}




class 		a_    (			nn.Module						):







    def __init__(      self      :							Optional[int]  ,       __UpperCamelCase      :							Any  )		->List[str]:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase        = torchvision.models.resnetaaa(pretrained=__UpperCamelCase  )
        _UpperCAmelCase        = list(model.children()  )[:-2]
        _UpperCAmelCase        = nn.Sequential(*__UpperCamelCase  )
        _UpperCAmelCase        = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.pool(self.model(__UpperCamelCase  )  )
        _UpperCAmelCase        = torch.flatten(__UpperCamelCase  ,       start_dim=2  )
        _UpperCAmelCase        = out.transpose(1  ,       2  ).contiguous()
        return out  # BxNx2048







class 		a_    (			_UpperCAmelCase						):







    def __init__(      self      :							List[Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Tuple  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = [json.loads(__UpperCamelCase  ) for l in open(__UpperCamelCase  )]
        _UpperCAmelCase        = os.path.dirname(__UpperCamelCase  )
        _UpperCAmelCase        = tokenizer
        _UpperCAmelCase        = labels
        _UpperCAmelCase        = len(__UpperCamelCase  )
        _UpperCAmelCase        = max_seq_length

        _UpperCAmelCase        = transforms







    def __len__(      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        return len(self.data  )







    def __getitem__(      self      :							Optional[int]  ,       __UpperCamelCase      :							List[Any]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__UpperCamelCase  )  )
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = sentence[0], sentence[1:-1], sentence[-1]
        _UpperCAmelCase        = sentence[: self.max_seq_length]

        _UpperCAmelCase        = torch.zeros(self.n_classes  )
        _UpperCAmelCase        = 1

        _UpperCAmelCase        = Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]  )  ).convert("""RGB"""  )
        _UpperCAmelCase        = self.transforms(__UpperCamelCase  )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }







    def   _snake_case       (      self      :							List[Any]  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = Counter()
        for row in self.data:
            label_freqs.update(row["""label"""]  )
        return label_freqs







def 			_UpperCamelCase						(    _A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = [len(row["""sentence"""]   ) for row in batch]
    _UpperCAmelCase     ,_UpperCAmelCase        = len(_A   ), max(_A   )

    _UpperCAmelCase        = torch.zeros(_A       ,   _A       ,   dtype=torch.long   )
    _UpperCAmelCase        = torch.zeros(_A       ,   _A       ,   dtype=torch.long   )

    for i_batch, (input_row, length) in enumerate(zip(_A       ,   _A   )   ):
        _UpperCAmelCase        = input_row["""sentence"""]
        _UpperCAmelCase        = 1

    _UpperCAmelCase        = torch.stack([row["""image"""] for row in batch]   )
    _UpperCAmelCase        = torch.stack([row["""label"""] for row in batch]   )
    _UpperCAmelCase        = torch.stack([row["""image_start_token"""] for row in batch]   )
    _UpperCAmelCase        = torch.stack([row["""image_end_token"""] for row in batch]   )

    return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor


def 			_UpperCamelCase						(    )     ->	Union[str, Any]:





    """simple docstring"""

    return [
        "Crime",
        "Drama",
        "Thriller",
        "Action",
        "Comedy",
        "Romance",
        "Documentary",
        "Short",
        "Mystery",
        "History",
        "Family",
        "Adventure",
        "Fantasy",
        "Sci-Fi",
        "Western",
        "Horror",
        "Sport",
        "War",
        "Music",
        "Musical",
        "Animation",
        "Biography",
        "Film-Noir",
    ]







def 			_UpperCamelCase						(    )     ->	str:





    """simple docstring"""

    return transforms.Compose(
        [
            transforms.Resize(2_5_6   ),
            transforms.CenterCrop(2_2_4   ),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]       ,   std=[0.12_221_994, 0.12_145_835, 0.14_380_469]       ,   ),
        ]   )

"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()